11.1 Content
Content is structured to alternate between technical aspects (basics, hands-on exercises) and their relation to social work and practice.
#BFH Struktur
Preparation (Samin erstellt Begrüssungsemail)
- Begrüssungsnachricht: Download r/rstudio, videolink, aufgabe
- Some video, introduction course (Samin sucht video)
- Find examples of how data science can help social work: sie sollen ein Forumsbeitrag erstellen
Introduction (Dorian/Samin, 21.5.)
- What is data science and how can it help social work? What are the limits? (Slide 4 bis 7)
- Data that are relevant for social work
- Datafication:
- data collected for research/official statistics,
- found data (uebung: studierende sollen selber überlegen, wo found data entstehen)
- Datafication:
- Crash course in R and R Studio: AI supported coding (ChatGPT-4 / Copilot), warum R, R as a calculator/objects/for loops (kurz), reading in data, überblick über datensatz (summary, nrow,…), dplyr, filter/select, mutate, rbind/merge, reshaping (?), umgang mit textdaten (?), saving
Descriptive analyses (Samin, 27.5.)
Prediction of risks (Dorian, 28.5)
Evaluation/Causal design/Effects of social work (Dorian, 3.6.)
- Evtl. 1/2 Tag oder 3/4 Tag
- Randomized control trials
- Natural experiments
Case studies mit Coaching (Dorian, 4.6.)
Beispieldaten
- Beispieldaten oben einbauen
- Richtungswechsel
- Swiss Household Panel (Samin lädt herunter)
- Data dashboard Wohlen; extension or data analysis of dependency duration (Dorian fragt nach; wir sagen nicht von welcher Gemeinde, Vertragliche Abmachung)
- Jugendarbeit Burgdorf (Dorian schreibt Mani)
- Fokus Arbeit (?): Wenn andere Daten nicht verfügbar sind
- ESS (Samin lädt herunter) ..Daten die relevant sind für die SA
- Open data/BFS/…Daten die relevant sind für die SA
Struktur KNW
- 5000-7000 Zeichen
- Beschreibung Fragestellung, Daten, Code, Resultate (Tabellen/Grafik), Interpretation (Interpretation der Ergebnisse, Limitation der Daten/Analysen/Schlussfolgerungen)
Vorbereitung von Tutorials/Videos/Aufträge etc. im Falle von Krankheit…https://www.theanalysisfactor.com/resources/
#FHNW Struktur - Introduction (Samin, 3.6.) -
Organisation: Gruppenformierung - What is data science and how can it
help social work? What are the limits? (Slide 4 bis 7) - Data that are
relevant for social work - Datafication:
- data collected for research/official statistics, - found data (uebung:
studierende sollen selber überlegen, wo found data entstehen) -
excercise: measuring social work outcome dimensions with found data
(give example) - risks? - Crash course in R and R Studio: AI supported
coding (GPT-4/Bing, Copilot), warum R, R as a calculator/objects (kurz),
reading in data, überblick über datensatz (summary, nrow,…), dplyr,
filter/select, mutate, merging, reshaping, umgang mit textdaten, saving
- Descriptive analyses (Samin, 4.6.) - Study designs:
descriptive, prediction, evaluation/causal design - Descriptive:
Monitoring, Probleme identifizieren, Überblick gewinnen, Entwicklungen
beschreiben - Prediction: (ungewünschtes) Vorhersagen -
Evaluation/Causal design: Wirkungen messen, Lösungen identifizieren -
Distributional characteristics/univariate statistics: Frequencies, mean,
median, modal value, variance, interquartile range, range of values. -
Correlations/bi-/multivariate statistics: correlation measures,
associations and group comparisons using regression or other statistical
techniques. - Einfach halten: Verweis auf Methodenberatungswebseite UZH
- Uncertainty: statistical significance, p-value and confidence
intervals. - Einfach und kurz - Results communication: tables with
quick_docx huxtable, ggplot2, (shiny; erwähnen) - Prediction of
risks, Evaluation/Causal design/Effects of social work (Dorian,
5.6) - Case study work with coaching (Dorian,
6.6) - Presentation
11.1.1 Technical Part
- Study Designs
- Description, prediction, causal inference/evaluation design.
- Measurement Theory
- Survey Data
- (Note: This might not be necessary since it is covered in standard curricula.)
- Crash Course: Interface of R/Python
- Basic functions, reading in and preparing administrative data, survey data, or data from organisations using R/Python.
- Doing simple calculations, creating descriptive tables and simple graphs.
- Descriptive Statistics:
- Frequencies.
- Mean, median, modal value.
- Variance, interquartile range, range of values.
- Correlation measures.
- Associations and group comparisons using regression or other statistical techniques.
- Machine Learning Applications